Overview

Dataset statistics

Number of variables15
Number of observations8655
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 MiB
Average record size in memory425.8 B

Variable types

Categorical5
Boolean2
Numeric8

Alerts

tipo_residencia is highly imbalanced (72.7%)Imbalance
qtd_filhos has 5486 (63.4%) zerosZeros

Reproduction

Analysis started2024-06-27 21:07:50.042036
Analysis finished2024-06-27 21:07:57.369572
Duration7.33 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

sexo
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size490.4 KiB
F
5491 
M
3164 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8655
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowM
3rd rowF
4th rowF
5th rowM

Common Values

ValueCountFrequency (%)
F 5491
63.4%
M 3164
36.6%

Length

2024-06-27T18:07:57.437069image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-27T18:07:57.529214image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
f 5491
63.4%
m 3164
36.6%

Most occurring characters

ValueCountFrequency (%)
F 5491
63.4%
M 3164
36.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 8655
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 5491
63.4%
M 3164
36.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 8655
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 5491
63.4%
M 3164
36.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8655
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 5491
63.4%
M 3164
36.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.6 KiB
False
5011 
True
3644 
ValueCountFrequency (%)
False 5011
57.9%
True 3644
42.1%
2024-06-27T18:07:57.623346image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.6 KiB
True
5657 
False
2998 
ValueCountFrequency (%)
True 5657
65.4%
False 2998
34.6%
2024-06-27T18:07:57.714777image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

qtd_filhos
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.51611785
Minimum0
Maximum14
Zeros5486
Zeros (%)63.4%
Negative0
Negative (%)0.0%
Memory size67.7 KiB
2024-06-27T18:07:57.800650image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum14
Range14
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.79615885
Coefficient of variation (CV)1.5425912
Kurtosis20.701327
Mean0.51611785
Median Absolute Deviation (MAD)0
Skewness2.4591857
Sum4467
Variance0.63386891
MonotonicityNot monotonic
2024-06-27T18:07:57.899991image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 5486
63.4%
1 2064
 
23.8%
2 959
 
11.1%
3 127
 
1.5%
4 13
 
0.2%
7 2
 
< 0.1%
14 2
 
< 0.1%
5 2
 
< 0.1%
ValueCountFrequency (%)
0 5486
63.4%
1 2064
 
23.8%
2 959
 
11.1%
3 127
 
1.5%
4 13
 
0.2%
5 2
 
< 0.1%
7 2
 
< 0.1%
14 2
 
< 0.1%
ValueCountFrequency (%)
14 2
 
< 0.1%
7 2
 
< 0.1%
5 2
 
< 0.1%
4 13
 
0.2%
3 127
 
1.5%
2 959
 
11.1%
1 2064
 
23.8%
0 5486
63.4%

tipo_renda
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size697.3 KiB
Assalariado
5364 
Empresário
2398 
Servidor público
881 
Bolsista
 
7
Pensionista
 
5

Length

Max length16
Median length11
Mean length11.229463
Min length8

Characters and Unicode

Total characters97191
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEmpresário
2nd rowAssalariado
3rd rowEmpresário
4th rowServidor público
5th rowAssalariado

Common Values

ValueCountFrequency (%)
Assalariado 5364
62.0%
Empresário 2398
27.7%
Servidor público 881
 
10.2%
Bolsista 7
 
0.1%
Pensionista 5
 
0.1%

Length

2024-06-27T18:07:58.028696image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-27T18:07:58.138820image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
assalariado 5364
56.2%
empresário 2398
25.1%
servidor 881
 
9.2%
público 881
 
9.2%
bolsista 7
 
0.1%
pensionista 5
 
0.1%

Most occurring characters

ValueCountFrequency (%)
a 16104
16.6%
s 13150
13.5%
r 11922
12.3%
i 9541
9.8%
o 9536
9.8%
l 6252
 
6.4%
d 6245
 
6.4%
A 5364
 
5.5%
e 3284
 
3.4%
p 3279
 
3.4%
Other values (13) 12514
12.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 87655
90.2%
Uppercase Letter 8655
 
8.9%
Space Separator 881
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 16104
18.4%
s 13150
15.0%
r 11922
13.6%
i 9541
10.9%
o 9536
10.9%
l 6252
 
7.1%
d 6245
 
7.1%
e 3284
 
3.7%
p 3279
 
3.7%
m 2398
 
2.7%
Other values (7) 5944
 
6.8%
Uppercase Letter
ValueCountFrequency (%)
A 5364
62.0%
E 2398
27.7%
S 881
 
10.2%
B 7
 
0.1%
P 5
 
0.1%
Space Separator
ValueCountFrequency (%)
881
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 96310
99.1%
Common 881
 
0.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 16104
16.7%
s 13150
13.7%
r 11922
12.4%
i 9541
9.9%
o 9536
9.9%
l 6252
 
6.5%
d 6245
 
6.5%
A 5364
 
5.6%
e 3284
 
3.4%
p 3279
 
3.4%
Other values (12) 11633
12.1%
Common
ValueCountFrequency (%)
881
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 93912
96.6%
None 3279
 
3.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 16104
17.1%
s 13150
14.0%
r 11922
12.7%
i 9541
10.2%
o 9536
10.2%
l 6252
 
6.7%
d 6245
 
6.6%
A 5364
 
5.7%
e 3284
 
3.5%
p 3279
 
3.5%
Other values (11) 9235
9.8%
None
ValueCountFrequency (%)
á 2398
73.1%
ú 881
 
26.9%

educacao
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size766.3 KiB
Secundário
4868 
Superior completo
3340 
Superior incompleto
 
367
Primário
 
66
Pós graduação
 
14

Length

Max length19
Median length10
Mean length13.072559
Min length8

Characters and Unicode

Total characters113143
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSecundário
2nd rowSuperior completo
3rd rowSuperior completo
4th rowSuperior completo
5th rowSecundário

Common Values

ValueCountFrequency (%)
Secundário 4868
56.2%
Superior completo 3340
38.6%
Superior incompleto 367
 
4.2%
Primário 66
 
0.8%
Pós graduação 14
 
0.2%

Length

2024-06-27T18:07:58.265336image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-27T18:07:58.370302image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
secundário 4868
39.3%
superior 3707
30.0%
completo 3340
27.0%
incompleto 367
 
3.0%
primário 66
 
0.5%
pós 14
 
0.1%
graduação 14
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o 16069
14.2%
r 12428
11.0%
e 12282
10.9%
i 9074
8.0%
u 8589
7.6%
S 8575
7.6%
c 8575
7.6%
p 7414
 
6.6%
n 5235
 
4.6%
á 4934
 
4.4%
Other values (12) 19968
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 100767
89.1%
Uppercase Letter 8655
 
7.6%
Space Separator 3721
 
3.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 16069
15.9%
r 12428
12.3%
e 12282
12.2%
i 9074
9.0%
u 8589
8.5%
c 8575
8.5%
p 7414
7.4%
n 5235
 
5.2%
á 4934
 
4.9%
d 4882
 
4.8%
Other values (9) 11285
11.2%
Uppercase Letter
ValueCountFrequency (%)
S 8575
99.1%
P 80
 
0.9%
Space Separator
ValueCountFrequency (%)
3721
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 109422
96.7%
Common 3721
 
3.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 16069
14.7%
r 12428
11.4%
e 12282
11.2%
i 9074
8.3%
u 8589
7.8%
S 8575
7.8%
c 8575
7.8%
p 7414
6.8%
n 5235
 
4.8%
á 4934
 
4.5%
Other values (11) 16247
14.8%
Common
ValueCountFrequency (%)
3721
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 108167
95.6%
None 4976
 
4.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 16069
14.9%
r 12428
11.5%
e 12282
11.4%
i 9074
8.4%
u 8589
7.9%
S 8575
7.9%
c 8575
7.9%
p 7414
6.9%
n 5235
 
4.8%
d 4882
 
4.5%
Other values (8) 15044
13.9%
None
ValueCountFrequency (%)
á 4934
99.2%
ó 14
 
0.3%
ç 14
 
0.3%
ã 14
 
0.3%

estado_civil
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size562.1 KiB
Casado
6150 
Solteiro
1069 
União
679 
Separado
 
535
Viúvo
 
222

Length

Max length8
Median length6
Mean length6.2665511
Min length5

Characters and Unicode

Total characters54237
Distinct characters18
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSolteiro
2nd rowCasado
3rd rowCasado
4th rowCasado
5th rowSolteiro

Common Values

ValueCountFrequency (%)
Casado 6150
71.1%
Solteiro 1069
 
12.4%
União 679
 
7.8%
Separado 535
 
6.2%
Viúvo 222
 
2.6%

Length

2024-06-27T18:07:58.496309image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-27T18:07:58.611018image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
casado 6150
71.1%
solteiro 1069
 
12.4%
união 679
 
7.8%
separado 535
 
6.2%
viúvo 222
 
2.6%

Most occurring characters

ValueCountFrequency (%)
a 13370
24.7%
o 9724
17.9%
d 6685
12.3%
C 6150
11.3%
s 6150
11.3%
i 1970
 
3.6%
e 1604
 
3.0%
S 1604
 
3.0%
r 1604
 
3.0%
l 1069
 
2.0%
Other values (8) 4307
 
7.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 45582
84.0%
Uppercase Letter 8655
 
16.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 13370
29.3%
o 9724
21.3%
d 6685
14.7%
s 6150
13.5%
i 1970
 
4.3%
e 1604
 
3.5%
r 1604
 
3.5%
l 1069
 
2.3%
t 1069
 
2.3%
n 679
 
1.5%
Other values (4) 1658
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
C 6150
71.1%
S 1604
 
18.5%
U 679
 
7.8%
V 222
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 54237
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 13370
24.7%
o 9724
17.9%
d 6685
12.3%
C 6150
11.3%
s 6150
11.3%
i 1970
 
3.6%
e 1604
 
3.0%
S 1604
 
3.0%
r 1604
 
3.0%
l 1069
 
2.0%
Other values (8) 4307
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 53336
98.3%
None 901
 
1.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 13370
25.1%
o 9724
18.2%
d 6685
12.5%
C 6150
11.5%
s 6150
11.5%
i 1970
 
3.7%
e 1604
 
3.0%
S 1604
 
3.0%
r 1604
 
3.0%
l 1069
 
2.0%
Other values (6) 3406
 
6.4%
None
ValueCountFrequency (%)
ã 679
75.4%
ú 222
 
24.6%

tipo_residencia
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size525.6 KiB
Casa
7698 
Com os pais
 
472
Governamental
 
252
Aluguel
 
128
Estúdio
 
61

Length

Max length13
Median length4
Mean length4.7448873
Min length4

Characters and Unicode

Total characters41067
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCasa
2nd rowCasa
3rd rowCasa
4th rowCasa
5th rowGovernamental

Common Values

ValueCountFrequency (%)
Casa 7698
88.9%
Com os pais 472
 
5.5%
Governamental 252
 
2.9%
Aluguel 128
 
1.5%
Estúdio 61
 
0.7%
Comunitário 44
 
0.5%

Length

2024-06-27T18:07:58.726501image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-27T18:07:58.831894image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
casa 7698
80.2%
com 472
 
4.9%
os 472
 
4.9%
pais 472
 
4.9%
governamental 252
 
2.6%
aluguel 128
 
1.3%
estúdio 61
 
0.6%
comunitário 44
 
0.5%

Most occurring characters

ValueCountFrequency (%)
a 16372
39.9%
s 8703
21.2%
C 8214
20.0%
o 1345
 
3.3%
944
 
2.3%
m 768
 
1.9%
e 632
 
1.5%
i 621
 
1.5%
n 548
 
1.3%
l 508
 
1.2%
Other values (12) 2412
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 31468
76.6%
Uppercase Letter 8655
 
21.1%
Space Separator 944
 
2.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 16372
52.0%
s 8703
27.7%
o 1345
 
4.3%
m 768
 
2.4%
e 632
 
2.0%
i 621
 
2.0%
n 548
 
1.7%
l 508
 
1.6%
p 472
 
1.5%
t 357
 
1.1%
Other values (7) 1142
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
C 8214
94.9%
G 252
 
2.9%
A 128
 
1.5%
E 61
 
0.7%
Space Separator
ValueCountFrequency (%)
944
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 40123
97.7%
Common 944
 
2.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 16372
40.8%
s 8703
21.7%
C 8214
20.5%
o 1345
 
3.4%
m 768
 
1.9%
e 632
 
1.6%
i 621
 
1.5%
n 548
 
1.4%
l 508
 
1.3%
p 472
 
1.2%
Other values (11) 1940
 
4.8%
Common
ValueCountFrequency (%)
944
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40962
99.7%
None 105
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 16372
40.0%
s 8703
21.2%
C 8214
20.1%
o 1345
 
3.3%
944
 
2.3%
m 768
 
1.9%
e 632
 
1.5%
i 621
 
1.5%
n 548
 
1.3%
l 508
 
1.2%
Other values (10) 2307
 
5.6%
None
ValueCountFrequency (%)
ú 61
58.1%
á 44
41.9%

idade
Real number (ℝ)

Distinct46
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.7316
Minimum22
Maximum67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.7 KiB
2024-06-27T18:07:58.963264image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile27
Q133
median40
Q348
95-th percentile57
Maximum67
Range45
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.3794241
Coefficient of variation (CV)0.23027389
Kurtosis-0.77727369
Mean40.7316
Median Absolute Deviation (MAD)7
Skewness0.24648417
Sum352532
Variance87.973597
MonotonicityNot monotonic
2024-06-27T18:07:59.094677image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
40 369
 
4.3%
39 323
 
3.7%
37 317
 
3.7%
43 315
 
3.6%
32 315
 
3.6%
33 306
 
3.5%
41 300
 
3.5%
34 300
 
3.5%
28 294
 
3.4%
38 293
 
3.4%
Other values (36) 5523
63.8%
ValueCountFrequency (%)
22 10
 
0.1%
23 19
 
0.2%
24 70
 
0.8%
25 91
 
1.1%
26 123
1.4%
27 286
3.3%
28 294
3.4%
29 257
3.0%
30 287
3.3%
31 268
3.1%
ValueCountFrequency (%)
67 1
 
< 0.1%
66 6
 
0.1%
65 4
 
< 0.1%
64 25
 
0.3%
63 28
 
0.3%
62 35
 
0.4%
61 28
 
0.3%
60 92
1.1%
59 73
0.8%
58 71
0.8%

tempo_emprego
Real number (ℝ)

Distinct2589
Distinct (%)29.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7959767
Minimum0.11780822
Maximum42.906849
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.7 KiB
2024-06-27T18:07:59.231880image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.11780822
5-th percentile0.72520548
Q13.0465753
median6.0520548
Q310.268493
95-th percentile21.550685
Maximum42.906849
Range42.789041
Interquartile range (IQR)7.2219178

Descriptive statistics

Standard deviation6.7404803
Coefficient of variation (CV)0.86461012
Kurtosis3.444574
Mean7.7959767
Median Absolute Deviation (MAD)3.4219178
Skewness1.6723556
Sum67474.178
Variance45.434075
MonotonicityNot monotonic
2024-06-27T18:07:59.367481image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.216438356 25
 
0.3%
6.934246575 20
 
0.2%
1.098630137 20
 
0.2%
5.717808219 18
 
0.2%
3.602739726 16
 
0.2%
4.517808219 16
 
0.2%
4.964383562 16
 
0.2%
4.931506849 16
 
0.2%
7.520547945 16
 
0.2%
4.597260274 15
 
0.2%
Other values (2579) 8477
97.9%
ValueCountFrequency (%)
0.1178082192 1
 
< 0.1%
0.1780821918 1
 
< 0.1%
0.2 3
< 0.1%
0.2164383562 1
 
< 0.1%
0.2410958904 1
 
< 0.1%
0.2438356164 2
 
< 0.1%
0.2493150685 2
 
< 0.1%
0.2520547945 1
 
< 0.1%
0.2547945205 6
0.1%
0.2602739726 4
< 0.1%
ValueCountFrequency (%)
42.90684932 2
 
< 0.1%
41.2 11
0.1%
40.78630137 2
 
< 0.1%
40.57534247 6
0.1%
39.82465753 4
 
< 0.1%
39.65205479 3
 
< 0.1%
39.48767123 1
 
< 0.1%
39.28219178 1
 
< 0.1%
38.40547945 1
 
< 0.1%
36.86575342 2
 
< 0.1%

qt_pessoas_residencia
Real number (ℝ)

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3047949
Minimum1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.7 KiB
2024-06-27T18:07:59.468814image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum15
Range14
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.94644867
Coefficient of variation (CV)0.41064333
Kurtosis8.1777357
Mean2.3047949
Median Absolute Deviation (MAD)0
Skewness1.3304013
Sum19948
Variance0.89576508
MonotonicityNot monotonic
2024-06-27T18:07:59.574767image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2 4500
52.0%
3 1715
 
19.8%
1 1381
 
16.0%
4 917
 
10.6%
5 123
 
1.4%
6 14
 
0.2%
9 2
 
< 0.1%
15 2
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
1 1381
 
16.0%
2 4500
52.0%
3 1715
 
19.8%
4 917
 
10.6%
5 123
 
1.4%
6 14
 
0.2%
7 1
 
< 0.1%
9 2
 
< 0.1%
15 2
 
< 0.1%
ValueCountFrequency (%)
15 2
 
< 0.1%
9 2
 
< 0.1%
7 1
 
< 0.1%
6 14
 
0.2%
5 123
 
1.4%
4 917
 
10.6%
3 1715
 
19.8%
2 4500
52.0%
1 1381
 
16.0%

renda
Real number (ℝ)

Distinct8126
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6113.0778
Minimum118.71
Maximum245141.67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.7 KiB
2024-06-27T18:07:59.704473image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum118.71
5-th percentile962.968
Q12114.015
median3696.89
Q36824.885
95-th percentile17727.223
Maximum245141.67
Range245022.96
Interquartile range (IQR)4710.87

Descriptive statistics

Standard deviation9048.2806
Coefficient of variation (CV)1.4801514
Kurtosis133.50575
Mean6113.0778
Median Absolute Deviation (MAD)1976.29
Skewness8.5695652
Sum52908688
Variance81871382
MonotonicityNot monotonic
2024-06-27T18:07:59.850269image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9106.83 2
 
< 0.1%
2294.57 2
 
< 0.1%
2466.77 2
 
< 0.1%
7371.25 2
 
< 0.1%
740.32 2
 
< 0.1%
16911.02 2
 
< 0.1%
4523.84 2
 
< 0.1%
2386.69 2
 
< 0.1%
3582.35 2
 
< 0.1%
557.74 2
 
< 0.1%
Other values (8116) 8635
99.8%
ValueCountFrequency (%)
118.71 1
< 0.1%
211.04 1
< 0.1%
249.14 1
< 0.1%
275.11 1
< 0.1%
300.76 1
< 0.1%
307.48 1
< 0.1%
316.7 1
< 0.1%
319.78 1
< 0.1%
321.7 1
< 0.1%
327.51 2
< 0.1%
ValueCountFrequency (%)
245141.67 1
< 0.1%
179538.8 1
< 0.1%
172748.39 1
< 0.1%
166223.85 1
< 0.1%
154006.23 1
< 0.1%
140482.94 1
< 0.1%
121348.3 1
< 0.1%
119626.38 1
< 0.1%
107414.21 1
< 0.1%
102641.07 1
< 0.1%

renda_per_capita
Real number (ℝ)

Distinct8139
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3089.744
Minimum70.346667
Maximum122570.84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.7 KiB
2024-06-27T18:07:59.989628image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum70.346667
5-th percentile387.387
Q1936.71125
median1774.95
Q33433.5092
95-th percentile9531.782
Maximum122570.84
Range122500.49
Interquartile range (IQR)2496.7979

Descriptive statistics

Standard deviation4850.2522
Coefficient of variation (CV)1.569791
Kurtosis109.6327
Mean3089.744
Median Absolute Deviation (MAD)1018.46
Skewness7.9039666
Sum26741734
Variance23524946
MonotonicityNot monotonic
2024-06-27T18:08:00.140962image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
805.59 3
 
< 0.1%
1821.366 2
 
< 0.1%
581.45 2
 
< 0.1%
11506.64 2
 
< 0.1%
2564.1 2
 
< 0.1%
889.385 2
 
< 0.1%
1433.86 2
 
< 0.1%
1403.965 2
 
< 0.1%
5631.91 2
 
< 0.1%
2695.695 2
 
< 0.1%
Other values (8129) 8634
99.8%
ValueCountFrequency (%)
70.34666667 1
< 0.1%
74.388 1
< 0.1%
83.04666667 1
< 0.1%
92.8475 2
< 0.1%
98.67166667 1
< 0.1%
100.2533333 1
< 0.1%
104.9625 1
< 0.1%
109.9375 1
< 0.1%
111.0175 1
< 0.1%
111.4 1
< 0.1%
ValueCountFrequency (%)
122570.835 1
< 0.1%
89769.4 1
< 0.1%
86374.195 1
< 0.1%
83111.925 1
< 0.1%
77003.115 1
< 0.1%
70241.47 1
< 0.1%
64245.29 1
< 0.1%
60803.32 1
< 0.1%
59813.19 1
< 0.1%
57222.81 1
< 0.1%

tempo_emprego_idade_ratio
Real number (ℝ)

Distinct4124
Distinct (%)47.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.18883257
Minimum0.0022655427
Maximum0.69204596
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.7 KiB
2024-06-27T18:08:00.279292image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.0022655427
5-th percentile0.019081951
Q10.076883562
median0.15825523
Q30.26526952
95-th percentile0.48263014
Maximum0.69204596
Range0.68978041
Interquartile range (IQR)0.18838596

Descriptive statistics

Standard deviation0.14303674
Coefficient of variation (CV)0.75747916
Kurtosis0.61887199
Mean0.18883257
Median Absolute Deviation (MAD)0.089990387
Skewness1.02721
Sum1634.3459
Variance0.020459508
MonotonicityNot monotonic
2024-06-27T18:08:00.416718image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4063680118 15
 
0.2%
0.1885083714 13
 
0.2%
0.325463336 13
 
0.2%
0.4326384753 12
 
0.1%
0.1487722926 12
 
0.1%
0.2100801241 11
 
0.1%
0.6866666667 11
 
0.1%
0.4175490559 11
 
0.1%
0.1937110834 11
 
0.1%
0.07181996086 10
 
0.1%
Other values (4114) 8536
98.6%
ValueCountFrequency (%)
0.002265542677 1
< 0.1%
0.003846153846 1
< 0.1%
0.004507291206 1
< 0.1%
0.004605071408 1
< 0.1%
0.00521601686 2
< 0.1%
0.005324373223 1
< 0.1%
0.005379825654 1
< 0.1%
0.005381604697 1
< 0.1%
0.005405405405 1
< 0.1%
0.005479452055 1
< 0.1%
ValueCountFrequency (%)
0.6920459567 2
 
< 0.1%
0.6866666667 11
0.1%
0.6836561171 3
 
< 0.1%
0.6737803413 1
 
< 0.1%
0.6651695486 6
0.1%
0.6637442922 4
 
< 0.1%
0.6583170254 2
 
< 0.1%
0.6581278539 1
 
< 0.1%
0.6542573194 2
 
< 0.1%
0.6537606617 2
 
< 0.1%

log_renda
Real number (ℝ)

Distinct8126
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2667925
Minimum4.7766835
Maximum12.409592
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.7 KiB
2024-06-27T18:08:00.558826image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum4.7766835
5-th percentile6.8700202
Q17.6563441
median8.2152472
Q38.8283308
95-th percentile9.7828564
Maximum12.409592
Range7.632908
Interquartile range (IQR)1.1719866

Descriptive statistics

Standard deviation0.89746077
Coefficient of variation (CV)0.10856215
Kurtosis0.35992408
Mean8.2667925
Median Absolute Deviation (MAD)0.58793653
Skewness0.31130627
Sum71549.089
Variance0.80543583
MonotonicityNot monotonic
2024-06-27T18:08:00.701223image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.11677996 2
 
< 0.1%
7.738300741 2
 
< 0.1%
7.810664882 2
 
< 0.1%
8.905342577 2
 
< 0.1%
6.607082525 2
 
< 0.1%
9.735720759 2
 
< 0.1%
8.41711647 2
 
< 0.1%
7.777662748 2
 
< 0.1%
8.183774289 2
 
< 0.1%
6.323892904 2
 
< 0.1%
Other values (8116) 8635
99.8%
ValueCountFrequency (%)
4.776683544 1
< 0.1%
5.352047689 1
< 0.1%
5.518014987 1
< 0.1%
5.617171018 1
< 0.1%
5.706312605 1
< 0.1%
5.728410044 1
< 0.1%
5.757954954 1
< 0.1%
5.767633259 1
< 0.1%
5.773619434 1
< 0.1%
5.791518589 2
< 0.1%
ValueCountFrequency (%)
12.40959157 1
< 0.1%
12.09814662 1
< 0.1%
12.05959142 1
< 0.1%
12.02109065 1
< 0.1%
11.94474834 1
< 0.1%
11.85284134 1
< 0.1%
11.7064202 1
< 0.1%
11.69212866 1
< 0.1%
11.58444776 1
< 0.1%
11.53899342 1
< 0.1%

Interactions

2024-06-27T18:07:56.210041image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:50.275409image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:51.094958image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:51.813396image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:52.528044image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:53.295052image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:54.065424image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:55.417219image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:56.305404image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:50.414493image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:51.188698image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:51.907078image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:52.621505image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:53.388763image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:54.172520image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:55.526803image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:56.392264image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:50.511363image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:51.272820image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:51.989125image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:52.712755image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:53.475525image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:54.264935image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:55.617535image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:56.480595image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:50.597617image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:51.358536image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:52.068418image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:52.803123image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:53.575431image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:54.354100image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:55.708340image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:56.576200image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:50.694639image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:51.448835image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:52.158190image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:52.900382image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:53.676185image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:54.451468image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:55.806160image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:56.668401image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:50.799105image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:51.530819image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:52.246300image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:52.994216image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:53.767395image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:54.547291image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:55.905119image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:56.764816image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:50.898817image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:51.626070image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:52.337264image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:53.094436image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:53.862343image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:54.640299image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:56.009822image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:56.871244image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:50.999295image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:51.722171image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:52.432951image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:53.198888image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:53.964804image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:54.749152image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-27T18:07:56.109191image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Missing values

2024-06-27T18:07:57.032574image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-27T18:07:57.266253image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

sexoposse_de_veiculoposse_de_imovelqtd_filhostipo_rendaeducacaoestado_civiltipo_residenciaidadetempo_empregoqt_pessoas_residenciarendarenda_per_capitatempo_emprego_idade_ratiolog_renda
0FFalseTrue0EmpresárioSecundárioSolteiroCasa266.6027401.08060.348060.3400000.2539528.994711
1MTrueTrue0AssalariadoSuperior completoCasadoCasa287.1835622.01852.15926.0750000.2565567.524102
2FTrueTrue0EmpresárioSuperior completoCasadoCasa350.8383562.02253.891126.9450000.0239537.720413
3FFalseTrue1Servidor públicoSuperior completoCasadoCasa304.8465753.06600.772200.2566670.1615538.794942
4MTrueFalse0AssalariadoSecundárioSolteiroGovernamental334.2931511.06475.976475.9700000.1300958.775854
5FFalseTrue0AssalariadoSuperior completoCasadoCasa394.3452052.01445.87722.9350000.1114167.276466
6FFalseTrue0EmpresárioSuperior completoViúvoCasa556.3780821.01726.031726.0300000.1159657.453579
7FFalseTrue0EmpresárioSecundárioCasadoCasa363.1041102.02515.981257.9900000.0862257.830418
8FFalseTrue0AssalariadoSecundárioCasadoCasa5018.6054792.03420.341710.1700000.3721108.137495
9MTrueTrue0AssalariadoSuperior completoCasadoCasa6010.5589042.012939.146469.5700000.1759829.468012
sexoposse_de_veiculoposse_de_imovelqtd_filhostipo_rendaeducacaoestado_civiltipo_residenciaidadetempo_empregoqt_pessoas_residenciarendarenda_per_capitatempo_emprego_idade_ratiolog_renda
8645FFalseFalse1AssalariadoSecundárioCasadoCasa484.3041103.02265.91755.3033330.0896697.725732
8646FFalseTrue0AssalariadoSuperior completoSolteiroCasa559.5561641.04360.444360.4400000.1737488.380328
8647MFalseFalse0AssalariadoSecundárioSolteiroCom os pais243.2931511.02284.692284.6900000.1372157.733986
8648FTrueFalse0AssalariadoSuperior completoCasadoCasa373.4575342.0529.27264.6350000.0934476.271499
8649MTrueFalse2AssalariadoSecundárioCasadoCasa4119.6794524.03971.13992.7825000.4799878.286806
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